code: def test_data_binary_and_multilabel(request):
      return [
          # Binary input data of shape (N,) or (N, 1)
          (
              torch.randint(0, 2, size=(50,)).long(),
              torch.randint(0, 2, size=(50,)).long(),
              1,
          ),
          (
              torch.randint(0, 2, size=(50, 1)).long(),
              torch.randint(0, 2, size=(50, 1)).long(),
              1,
          ),
          # updated batches
          (
              torch.randint(0, 2, size=(50,)).long(),
              torch.randint(0, 2, size=(50,)).long(),
              16,
          ),
          (
              torch.randint(0, 2, size=(50, 1)).long(),
              torch.randint(0, 2, size=(50, 1)).long(),
              16,
          ),
          # Binary input data of shape (N, L)
          (
              torch.randint(0, 2, size=(50, 4)).long(),
              torch.randint(0, 2, size=(50, 4)).long(),
              1,
          ),
          (
              torch.randint(0, 2, size=(50, 7)).long(),
              torch.randint(0, 2, size=(50, 7)).long(),
              1,
          ),
          # updated batches
          (
              torch.randint(0, 2, size=(50, 4)).long(),
              torch.randint(0, 2, size=(50, 4)).long(),
              16,
          ),
          (
              torch.randint(0, 2, size=(50, 7)).long(),
              torch.randint(0, 2, size=(50, 7)).long(),
              16,
          ),
      ][request.param]
  
imports: import os
  from unittest.mock import patch
  import pytest
  import sklearn
  import torch
  from sklearn.metrics import average_precision_score
  import ignite.distributed
  from ignite.contrib.metrics import AveragePrecision
  from ignite.engine import Engine
  from ignite.exceptions import NotComputableError
label_desc: Use image node_id 6 for calling a global function with example usage: test_data_binary_and_multilabel(request) and returns: list